Total Time

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable       n  mean    sd
##   <fct>     <fct>      <dbl> <dbl> <dbl>
## 1 1         Total_Time    23  445. 161. 
## 2 2         Total_Time    23  206.  86.3
## 3 3         Total_Time    23  152.  90.4

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 4 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 1         BNC09             984.             35.8            948.    3088.
## 2 1         BNC28             189.             24.3            164.    1702.
## 3 1         BNC32             696.             37.6            658.    4336.
## 4 3         BNC09             381.             19.6            361.    2189.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

1 extreme outlier, but we will keep in the analysis for now.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable   statistic       p
##   <fct>     <chr>          <dbl>   <dbl>
## 1 1         Total_Time     0.866 0.00538
## 2 2         Total_Time     0.946 0.246  
## 3 3         Total_Time     0.890 0.0160

## ANOVA Table (type III tests)
## 
##      Effect  DFn   DFd      F        p p<.05  ges
## 1 Block_num 1.27 27.85 79.626 1.61e-10     * 0.55
## # A tibble: 3 × 10
##   .y.    group1 group2    n1    n2 statistic    df        p   p.adj p.adj.signif
## * <chr>  <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>   <dbl> <chr>       
## 1 Total… 1      2         23    23      7.95    22 6.49e- 8 1.95e-7 ****        
## 2 Total… 1      3         23    23     10.5     22 4.92e-10 1.48e-9 ****        
## 3 Total… 2      3         23    23      4.40    22 2.28e- 4 6.84e-4 ***

Orientation Time

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable             n  mean    sd
##   <fct>     <fct>            <dbl> <dbl> <dbl>
## 1 1         Orientation_Time    23  40.1 14.5 
## 2 2         Orientation_Time    23  24.9  7.09
## 3 3         Orientation_Time    23  20.1  7.38

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 5 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 1         BNC07            436.              91.5           345.     2567.
## 2 2         BNC12             68.5             11.9            56.7    1209.
## 3 2         BNC30            134.              35.3            98.5    1394.
## 4 2         BNC32            338.              48.8           290.     2205.
## 5 3         BNC32            318.              42.7           275.     2431.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

2 extreme outliers, but we will keep in the analysis for now.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable         statistic        p
##   <fct>     <chr>                <dbl>    <dbl>
## 1 1         Orientation_Time     0.821 0.000856
## 2 2         Orientation_Time     0.863 0.00470 
## 3 3         Orientation_Time     0.913 0.0480

## ANOVA Table (type III tests)
## 
##      Effect DFn   DFd      F        p p<.05   ges
## 1 Block_num 1.1 24.11 29.353 9.09e-06     * 0.419
## # A tibble: 3 × 10
##   .y.     group1 group2    n1    n2 statistic    df       p   p.adj p.adj.signif
## * <chr>   <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl> <chr>       
## 1 Orient… 1      2         23    23      4.51    22 1.72e-4 5.16e-4 ***         
## 2 Orient… 1      3         23    23      6.27    22 2.62e-6 7.86e-6 ****        
## 3 Orient… 2      3         23    23      5.60    22 1.24e-5 3.72e-5 ****

Distance

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable     n  mean    sd
##   <fct>     <fct>    <dbl> <dbl> <dbl>
## 1 1         Distance    23 2764.  818.
## 2 2         Distance    23 1778.  530.
## 3 3         Distance    23 1582.  565.

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 1 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 2         BNC08             272.             21.5            251.    3190.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable statistic       p
##   <fct>     <chr>        <dbl>   <dbl>
## 1 1         Distance     0.931 0.116  
## 2 2         Distance     0.884 0.0122 
## 3 3         Distance     0.830 0.00120

## ANOVA Table (type III tests)
## 
##      Effect  DFn  DFd      F        p p<.05   ges
## 1 Block_num 1.57 34.5 37.526 1.74e-08     * 0.398
## # A tibble: 3 × 10
##   .y.     group1 group2    n1    n2 statistic    df       p   p.adj p.adj.signif
## * <chr>   <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl> <chr>       
## 1 Distan… 1      2         23    23      6.21    22 2.96e-6 8.88e-6 ****        
## 2 Distan… 1      3         23    23      6.98    22 5.20e-7 1.56e-6 ****        
## 3 Distan… 2      3         23    23      1.92    22 6.8 e-2 2.03e-1 ns

Speed

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable     n  mean    sd
##   <fct>     <fct>    <dbl> <dbl> <dbl>
## 1 1         Speed       23  7.43  1.87
## 2 2         Speed       23 10.7   3.22
## 3 3         Speed       23 13.0   3.80

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 1 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 2         BNC12             68.5             11.9            56.7    1209.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable statistic     p
##   <fct>     <chr>        <dbl> <dbl>
## 1 1         Speed        0.975 0.804
## 2 2         Speed        0.967 0.615
## 3 3         Speed        0.979 0.880

## ANOVA Table (type III tests)
## 
##      Effect  DFn   DFd      F        p p<.05   ges
## 1 Block_num 1.49 32.73 74.908 1.21e-11     * 0.366
## # A tibble: 3 × 10
##   .y.   group1 group2    n1    n2 statistic    df         p   p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>     <dbl>   <dbl> <chr>       
## 1 Speed 1      2         23    23     -8.29    22   3.28e-8 9.84e-8 ****        
## 2 Speed 1      3         23    23     -9.67    22   2.21e-9 6.63e-9 ****        
## 3 Speed 2      3         23    23     -6.11    22   3.76e-6 1.13e-5 ****

Dwell Time

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable       n  mean    sd
##   <fct>     <fct>      <dbl> <dbl> <dbl>
## 1 1         Mean_Dwell    23 1.35  0.432
## 2 2         Mean_Dwell    23 0.906 0.287
## 3 3         Mean_Dwell    23 0.759 0.259

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 4 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 1         BNC09             984.             35.8            948.    3088.
## 2 1         BNC35             550.             45.0            505.    1868.
## 3 2         BNC35             217.             27.1            190.    1234.
## 4 3         BNC35             189.             26.3            163.    1222.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable   statistic       p
##   <fct>     <chr>          <dbl>   <dbl>
## 1 1         Mean_Dwell     0.879 0.00968
## 2 2         Mean_Dwell     0.942 0.196  
## 3 3         Mean_Dwell     0.903 0.0289

## ANOVA Table (type III tests)
## 
##      Effect  DFn  DFd     F        p p<.05   ges
## 1 Block_num 1.25 27.6 80.39 1.73e-10     * 0.369
## # A tibble: 3 × 10
##   .y.     group1 group2    n1    n2 statistic    df       p   p.adj p.adj.signif
## * <chr>   <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl> <chr>       
## 1 Mean_D… 1      2         23    23      9.14    22 6.02e-9 1.81e-8 ****        
## 2 Mean_D… 1      3         23    23      9.43    22 3.46e-9 1.04e-8 ****        
## 3 Mean_D… 2      3         23    23      5.27    22 2.74e-5 8.22e-5 ****

Teleportations

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable           n  mean    sd
##   <fct>     <fct>          <dbl> <dbl> <dbl>
## 1 1         Teleportations    23  379. 138. 
## 2 2         Teleportations    23  235.  92.5
## 3 3         Teleportations    23  206. 103.

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 1 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 2         BNC08             272.             21.5            251.    3190.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable       statistic       p
##   <fct>     <chr>              <dbl>   <dbl>
## 1 1         Teleportations     0.950 0.300  
## 2 2         Teleportations     0.897 0.0218 
## 3 3         Teleportations     0.834 0.00142

## ANOVA Table (type III tests)
## 
##      Effect DFn   DFd      F        p p<.05   ges
## 1 Block_num 1.6 35.11 30.416 1.28e-07     * 0.319
## # A tibble: 3 × 10
##   .y.     group1 group2    n1    n2 statistic    df       p   p.adj p.adj.signif
## * <chr>   <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl> <chr>       
## 1 Telepo… 1      2         23    23      5.71    22 9.55e-6 2.86e-5 ****        
## 2 Telepo… 1      3         23    23      6.25    22 2.76e-6 8.28e-6 ****        
## 3 Telepo… 2      3         23    23      1.69    22 1.05e-1 3.15e-1 ns

P Value Correction for Comparisons So Far

Variance X

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable     n  mean    sd
##   <fct>     <fct>    <dbl> <dbl> <dbl>
## 1 1         Var_X       23  9.37  3.18
## 2 2         Var_X       23  8.41  2.48
## 3 3         Var_X       23  8.05  2.27

Visualizing the data:

Looking at outliers and if they’re extreme:

##  [1] Block_num        Participant      Total_Time       Orientation_Time
##  [5] Navigation_Time  Distance         Speed            Mean_Dwell      
##  [9] Var_X            Var_Y            Var_Z            Teleportations  
## [13] is.outlier       is.extreme      
## <0 rows> (or 0-length row.names)

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable statistic      p
##   <fct>     <chr>        <dbl>  <dbl>
## 1 1         Var_X        0.920 0.0680
## 2 2         Var_X        0.940 0.179 
## 3 3         Var_X        0.976 0.832

## ANOVA Table (type III tests)
## 
##      Effect  DFn   DFd     F     p p<.05   ges
## 1 Block_num 1.47 32.37 7.236 0.005     * 0.043
## # A tibble: 3 × 10
##   .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Var_X 1      2         23    23      3.00    22 0.007 0.02  *           
## 2 Var_X 1      3         23    23      2.92    22 0.008 0.024 *           
## 3 Var_X 2      3         23    23      1.30    22 0.208 0.624 ns

Variance Y

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable     n  mean    sd
##   <fct>     <fct>    <dbl> <dbl> <dbl>
## 1 1         Var_Y       23  68.9  17.5
## 2 2         Var_Y       23  62.2  18.5
## 3 3         Var_Y       23  57.7  16.2

Visualizing the data:

Looking at outliers and if they’re extreme:

##  [1] Block_num        Participant      Total_Time       Orientation_Time
##  [5] Navigation_Time  Distance         Speed            Mean_Dwell      
##  [9] Var_X            Var_Y            Var_Z            Teleportations  
## [13] is.outlier       is.extreme      
## <0 rows> (or 0-length row.names)

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable statistic      p
##   <fct>     <chr>        <dbl>  <dbl>
## 1 1         Var_Y        0.955 0.369 
## 2 2         Var_Y        0.922 0.0744
## 3 3         Var_Y        0.956 0.388

## ANOVA Table (type III tests)
## 
##      Effect DFn DFd    F     p p<.05   ges
## 1 Block_num   2  44 4.35 0.019     * 0.067
## # A tibble: 3 × 10
##   .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Var_Y 1      2         23    23      1.93    22 0.067 0.2   ns          
## 2 Var_Y 1      3         23    23      2.72    22 0.012 0.037 *           
## 3 Var_Y 2      3         23    23      1.17    22 0.256 0.768 ns

Variance Z

Plot

Stats

Summary stats of the data:

## # A tibble: 3 × 5
##   Block_num variable     n  mean    sd
##   <fct>     <fct>    <dbl> <dbl> <dbl>
## 1 1         Var_Z       23  5.08 1.30 
## 2 2         Var_Z       23  4.64 0.951
## 3 3         Var_Z       23  4.56 1.10

Visualizing the data:

Looking at outliers and if they’re extreme:

## # A tibble: 2 × 14
##   Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
##   <fct>     <chr>            <dbl>            <dbl>           <dbl>    <dbl>
## 1 3         BNC03            195.              24.1           170.     2243.
## 2 3         BNC33             73.1             25.0            48.0    1055.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## #   Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>

No extreme outliers.

Checking normality assumption:

## # A tibble: 3 × 4
##   Block_num variable statistic      p
##   <fct>     <chr>        <dbl>  <dbl>
## 1 1         Var_Z        0.956 0.380 
## 2 2         Var_Z        0.884 0.0117
## 3 3         Var_Z        0.947 0.259

## ANOVA Table (type III tests)
## 
##      Effect  DFn   DFd     F     p p<.05   ges
## 1 Block_num 1.49 32.85 4.088 0.036     * 0.042
## # A tibble: 3 × 10
##   .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Var_Z 1      2         23    23     3.09     22 0.005 0.016 *           
## 2 Var_Z 1      3         23    23     2.14     22 0.044 0.132 ns          
## 3 Var_Z 2      3         23    23     0.407    22 0.688 1     ns

P Value Correction for Head Motion Comparisons